#
# BSD 3-Clause License
#
# Copyright (c) 2017 xxxx
# All rights reserved.
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
#   list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
#   this list of conditions and the following disclaimer in the documentation
#   and/or other materials provided with the distribution.
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# * Neither the name of the copyright holder nor the names of its
#   contributors may be used to endorse or promote products derived from
#   this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ============================================================================
#
# data loader
from __future__ import print_function, division
import glob
import torch
from skimage import io, transform, color
import numpy as np
import math
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from PIL import Image
#==========================dataset load==========================

class RescaleT(object):

	def __init__(self,output_size):
		assert isinstance(output_size,(int,tuple))
		self.output_size = output_size

	def __call__(self,sample):
		image, label = sample['image'],sample['label']

		h, w = image.shape[:2]

		if isinstance(self.output_size,int):
			if h > w:
				new_h, new_w = self.output_size*h/w,self.output_size
			else:
				new_h, new_w = self.output_size,self.output_size*w/h
		else:
			new_h, new_w = self.output_size

		new_h, new_w = int(new_h), int(new_w)

		# #resize the image to new_h x new_w and convert image from range [0,255] to [0,1]
		# img = transform.resize(image,(new_h,new_w),mode='constant')
		# lbl = transform.resize(label,(new_h,new_w),mode='constant', order=0, preserve_range=True)

		img = transform.resize(image,(self.output_size,self.output_size),mode='constant')
		lbl = transform.resize(label,(self.output_size,self.output_size),mode='constant', order=0, preserve_range=True)

		return {'image':img,'label':lbl}

class Rescale(object):

	def __init__(self,output_size):
		assert isinstance(output_size,(int,tuple))
		self.output_size = output_size

	def __call__(self,sample):
		image, label = sample['image'],sample['label']

		h, w = image.shape[:2]

		if isinstance(self.output_size,int):
			if h > w:
				new_h, new_w = self.output_size*h/w,self.output_size
			else:
				new_h, new_w = self.output_size,self.output_size*w/h
		else:
			new_h, new_w = self.output_size

		new_h, new_w = int(new_h), int(new_w)

		# #resize the image to new_h x new_w and convert image from range [0,255] to [0,1]
		img = transform.resize(image,(new_h,new_w),mode='constant')
		lbl = transform.resize(label,(new_h,new_w),mode='constant', order=0, preserve_range=True)

		return {'image':img,'label':lbl}

class CenterCrop(object):

	def __init__(self,output_size):
		assert isinstance(output_size, (int, tuple))
		if isinstance(output_size, int):
			self.output_size = (output_size, output_size)
		else:
			assert len(output_size) == 2
			self.output_size = output_size
	def __call__(self,sample):
		image, label = sample['image'], sample['label']

		h, w = image.shape[:2]
		new_h, new_w = self.output_size

		# print("h: %d, w: %d, new_h: %d, new_w: %d"%(h, w, new_h, new_w))
		assert((h >= new_h) and (w >= new_w))

		h_offset = int(math.floor((h - new_h)/2))
		w_offset = int(math.floor((w - new_w)/2))

		image = image[h_offset: h_offset + new_h, w_offset: w_offset + new_w]
		label = label[h_offset: h_offset + new_h, w_offset: w_offset + new_w]

		return {'image': image, 'label': label}

class RandomCrop(object):

	def __init__(self,output_size):
		assert isinstance(output_size, (int, tuple))
		if isinstance(output_size, int):
			self.output_size = (output_size, output_size)
		else:
			assert len(output_size) == 2
			self.output_size = output_size
	def __call__(self,sample):
		image, label = sample['image'], sample['label']

		h, w = image.shape[:2]
		new_h, new_w = self.output_size

		top = np.random.randint(0, h - new_h)
		left = np.random.randint(0, w - new_w)

		image = image[top: top + new_h, left: left + new_w]
		label = label[top: top + new_h, left: left + new_w]

		return {'image': image, 'label': label}

class ToTensor(object):
	"""Convert ndarrays in sample to Tensors."""

	def __call__(self, sample):

		image, label = sample['image'], sample['label']

		tmpImg = np.zeros((image.shape[0],image.shape[1],3))
		tmpLbl = np.zeros(label.shape)

		image = image/np.max(image)
		if(np.max(label)<1e-6):
			label = label
		else:
			label = label/np.max(label)

		if image.shape[2]==1:
			tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
			tmpImg[:,:,1] = (image[:,:,0]-0.485)/0.229
			tmpImg[:,:,2] = (image[:,:,0]-0.485)/0.229
		else:
			tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
			tmpImg[:,:,1] = (image[:,:,1]-0.456)/0.224
			tmpImg[:,:,2] = (image[:,:,2]-0.406)/0.225

		tmpLbl[:,:,0] = label[:,:,0]

		# change the r,g,b to b,r,g from [0,255] to [0,1]
		#transforms.Normalize(mean = (0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225))
		tmpImg = tmpImg.transpose((2, 0, 1))
		tmpLbl = label.transpose((2, 0, 1))

		return {'image': torch.from_numpy(tmpImg),
			'label': torch.from_numpy(tmpLbl)}

class ToTensorLab(object):
	"""Convert ndarrays in sample to Tensors."""
	def __init__(self,flag=0):
		self.flag = flag

	def __call__(self, sample):

		image, label = sample['image'], sample['label']

		tmpLbl = np.zeros(label.shape)

		if(np.max(label)<1e-6):
			label = label
		else:
			label = label/np.max(label)

		# change the color space
		if self.flag == 2: # with rgb and Lab colors
			tmpImg = np.zeros((image.shape[0],image.shape[1],6))
			tmpImgt = np.zeros((image.shape[0],image.shape[1],3))
			if image.shape[2]==1:
				tmpImgt[:,:,0] = image[:,:,0]
				tmpImgt[:,:,1] = image[:,:,0]
				tmpImgt[:,:,2] = image[:,:,0]
			else:
				tmpImgt = image
			tmpImgtl = color.rgb2lab(tmpImgt)

			# nomalize image to range [0,1]
			tmpImg[:,:,0] = (tmpImgt[:,:,0]-np.min(tmpImgt[:,:,0]))/(np.max(tmpImgt[:,:,0])-np.min(tmpImgt[:,:,0]))
			tmpImg[:,:,1] = (tmpImgt[:,:,1]-np.min(tmpImgt[:,:,1]))/(np.max(tmpImgt[:,:,1])-np.min(tmpImgt[:,:,1]))
			tmpImg[:,:,2] = (tmpImgt[:,:,2]-np.min(tmpImgt[:,:,2]))/(np.max(tmpImgt[:,:,2])-np.min(tmpImgt[:,:,2]))
			tmpImg[:,:,3] = (tmpImgtl[:,:,0]-np.min(tmpImgtl[:,:,0]))/(np.max(tmpImgtl[:,:,0])-np.min(tmpImgtl[:,:,0]))
			tmpImg[:,:,4] = (tmpImgtl[:,:,1]-np.min(tmpImgtl[:,:,1]))/(np.max(tmpImgtl[:,:,1])-np.min(tmpImgtl[:,:,1]))
			tmpImg[:,:,5] = (tmpImgtl[:,:,2]-np.min(tmpImgtl[:,:,2]))/(np.max(tmpImgtl[:,:,2])-np.min(tmpImgtl[:,:,2]))

			# tmpImg = tmpImg/(np.max(tmpImg)-np.min(tmpImg))

			tmpImg[:,:,0] = (tmpImg[:,:,0]-np.mean(tmpImg[:,:,0]))/np.std(tmpImg[:,:,0])
			tmpImg[:,:,1] = (tmpImg[:,:,1]-np.mean(tmpImg[:,:,1]))/np.std(tmpImg[:,:,1])
			tmpImg[:,:,2] = (tmpImg[:,:,2]-np.mean(tmpImg[:,:,2]))/np.std(tmpImg[:,:,2])
			tmpImg[:,:,3] = (tmpImg[:,:,3]-np.mean(tmpImg[:,:,3]))/np.std(tmpImg[:,:,3])
			tmpImg[:,:,4] = (tmpImg[:,:,4]-np.mean(tmpImg[:,:,4]))/np.std(tmpImg[:,:,4])
			tmpImg[:,:,5] = (tmpImg[:,:,5]-np.mean(tmpImg[:,:,5]))/np.std(tmpImg[:,:,5])

		elif self.flag == 1: #with Lab color
			tmpImg = np.zeros((image.shape[0],image.shape[1],3))

			if image.shape[2]==1:
				tmpImg[:,:,0] = image[:,:,0]
				tmpImg[:,:,1] = image[:,:,0]
				tmpImg[:,:,2] = image[:,:,0]
			else:
				tmpImg = image

			tmpImg = color.rgb2lab(tmpImg)

			# tmpImg = tmpImg/(np.max(tmpImg)-np.min(tmpImg))

			tmpImg[:,:,0] = (tmpImg[:,:,0]-np.min(tmpImg[:,:,0]))/(np.max(tmpImg[:,:,0])-np.min(tmpImg[:,:,0]))
			tmpImg[:,:,1] = (tmpImg[:,:,1]-np.min(tmpImg[:,:,1]))/(np.max(tmpImg[:,:,1])-np.min(tmpImg[:,:,1]))
			tmpImg[:,:,2] = (tmpImg[:,:,2]-np.min(tmpImg[:,:,2]))/(np.max(tmpImg[:,:,2])-np.min(tmpImg[:,:,2]))

			tmpImg[:,:,0] = (tmpImg[:,:,0]-np.mean(tmpImg[:,:,0]))/np.std(tmpImg[:,:,0])
			tmpImg[:,:,1] = (tmpImg[:,:,1]-np.mean(tmpImg[:,:,1]))/np.std(tmpImg[:,:,1])
			tmpImg[:,:,2] = (tmpImg[:,:,2]-np.mean(tmpImg[:,:,2]))/np.std(tmpImg[:,:,2])

		else: # with rgb color
			tmpImg = np.zeros((image.shape[0],image.shape[1],3))
			image = image/np.max(image)
			if image.shape[2]==1:
				tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
				tmpImg[:,:,1] = (image[:,:,0]-0.485)/0.229
				tmpImg[:,:,2] = (image[:,:,0]-0.485)/0.229
			else:
				tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
				tmpImg[:,:,1] = (image[:,:,1]-0.456)/0.224
				tmpImg[:,:,2] = (image[:,:,2]-0.406)/0.225



		tmpLbl[:,:,0] = label[:,:,0]

		# change the r,g,b to b,r,g from [0,255] to [0,1]
		#transforms.Normalize(mean = (0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225))
		tmpImg = tmpImg.transpose((2, 0, 1))
		tmpLbl = label.transpose((2, 0, 1))

		return {'image': torch.from_numpy(tmpImg),
			'label': torch.from_numpy(tmpLbl)}

class SalObjDataset(Dataset):
	def __init__(self,img_name_list,lbl_name_list,transform=None):
		# self.root_dir = root_dir
		# self.image_name_list = glob.glob(image_dir+'*.png')
		# self.label_name_list = glob.glob(label_dir+'*.png')
		self.image_name_list = img_name_list
		self.label_name_list = lbl_name_list
		self.transform = transform

	def __len__(self):
		return len(self.image_name_list)

	def __getitem__(self,idx):

		# image = Image.open(self.image_name_list[idx])#io.imread(self.image_name_list[idx])
		# label = Image.open(self.label_name_list[idx])#io.imread(self.label_name_list[idx])

		image = io.imread(self.image_name_list[idx])

		if(0==len(self.label_name_list)):
			label_3 = np.zeros(image.shape)
		else:
			label_3 = io.imread(self.label_name_list[idx])

		#print("len of label3")
		#print(len(label_3.shape))
		#print(label_3.shape)

		label = np.zeros(label_3.shape[0:2])
		if(3==len(label_3.shape)):
			label = label_3[:,:,0]
		elif(2==len(label_3.shape)):
			label = label_3

		if(3==len(image.shape) and 2==len(label.shape)):
			label = label[:,:,np.newaxis]
		elif(2==len(image.shape) and 2==len(label.shape)):
			image = image[:,:,np.newaxis]
			label = label[:,:,np.newaxis]

		# #vertical flipping
		# # fliph = np.random.randn(1)
		# flipv = np.random.randn(1)
		#
		# if flipv>0:
		# 	image = image[::-1,:,:]
		# 	label = label[::-1,:,:]
		# #vertical flip

		sample = {'image':image, 'label':label}

		if self.transform:
			sample = self.transform(sample)

		return sample
